A new look at the automatic mapping between Arabic distinctive phonetic features and acoustic cues

In this paper, the multidimensional phonological feature structure of Arabic is investigated. Our goal is to assess the performance of statistical and connectionist approaches in performing the complex mappings between distinctive phonetic features (DPF) and associated acoustic cues. The present study explores the mapping between 29 phonological voicing, place, and manner features and Mel-frequency acoustic cues. For this purpose, three machine-learning techniques are deployed: Deep Belief Networks (DBN), Multilayer Perceptron (MLP), and Hidden Markov Models (HMM). The three techniques show satisfactory acoustic-phonetic mapping performance and indicate that couple of Arabic DPF elements such as affricatives, alveopalatals, labiodentals, lateral, palatal, pharyngeal, rounded, and uvular have a strong correlation with the acoustic information. The implications of these results on Arabic phonological contrasts are discussed.